Causal analysis is plausible reasoning applied to diagnosing observed effect(s), for example, diagnosing cause of biological impairment in a stream. Sir Bradford Hill basically defined the application of causal analysis when he enumerated the elements of causality for associating cigarette smoking with lung cancer. The identification of the true cause for an effect can be extremely difficult, if not impossible, to prove. However, by putting together a series of elements that must be satisfied, a plausible case can be made on the possible cause of the observed effect(s). The primary objective of EMAP has been to focus on condition of the aquatic resource, then to acquire a suite of exposure and habitat indicators, which can be used for diagnostics. The site selection for EMAP sampling is probability based, ensuring unbiased estimation of condition and of the stressors associated with condition.. EMAP data are thus well suited for use in plausible reasoning for identifying potential cause(s) of impaired aquatic resources. Possible criteria for the EMAP data to be appropriate for a specific causal analysis are (1) a response threshold must exist which identifies impairment of the aquatic resource, (2) the indicator of condition must be responsive over the range of the potential indicators of cause, and (3) the spatial density of the data must be adequate for the specific problem. Analysis tools used in the diagnostics of causal analysis include scatter plots, cumulative distribution functions, least-squares regressions, quantile regressions, and conditional probability analysis, Examples of applying theses tools with data for streams and estuaries are used to illustrate how probability surveys can contribute to diagnostics.